FPGAs have been an emerging topic on the application acceleration front over the last couple of years, but despite increased attention around use cases in machine learning and other hot areas, hands have been tied due to simply on-boarding with both the hardware and software. …
Every new hardware device that offers some kind of benefit compared to legacy devices faces the task of overcoming the immense inertia that is imparted to a platform by the software that runs upon it. …
While much of the work at Baidu we have focused on this year has centered on the Chinese search giant’s deep learning initiatives, many other critical, albeit less bleeding edge applications present true big data challenges. …
Over the last couple of years, the idea that the most efficient and high performance way to accelerate deep learning training and inference is with a custom ASIC—something designed to fit the specific needs of modern frameworks. …
At the last five annual Supercomputing Conferences, an underlying theme has been the potential of accelerators. …
IBM did not just stake the future of its Power chip and the systems business on which it depends on the OpenPower Foundation, a consortium now with 160 members after more than two years of cultivation by Big Blue and its key early partners – Google, Nvidia, Mellanox Technologies, and Tyan. …
There has been a noticeable interest uptick in reconfigurable computing devices, most notably, field programmable gate arrays (FPGAs). …
As we touched on last week, field programmable gate arrays (FPGAs) are not a silver bullet that will rip through the enterprise datacenter world anytime soon. …
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